Qwen2.5-VL-3B-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 3 Billion |
| Model Type | Vision-Language Model |
| Architecture | Transformer-based with ViT vision encoder |
| License | Open Source |
| Hugging Face | Qwen/Qwen2.5-VL-3B-Instruct |
What is Qwen2.5-VL-3B-Instruct-GGUF?
Qwen2.5-VL-3B-Instruct-GGUF is a compressed and optimized version of the Qwen2.5-VL vision-language model, specifically converted to GGUF format for efficient deployment. This model represents a significant advancement in multimodal AI, capable of understanding both images and videos while maintaining high performance in a relatively compact 3B parameter size.
Implementation Details
The model features a streamlined vision encoder with strategic implementation of window attention in the ViT architecture, optimized with SwiGLU and RMSNorm. It supports various quantization formats from 4-bit to 8-bit, enabling deployment across different hardware configurations. The model includes dynamic resolution and frame rate training for enhanced video understanding, with support for context lengths up to 32,768 tokens.
- Multiple quantization options (Q4_K to Q8_0) for different memory-performance tradeoffs
- Supports both CPU and GPU inference through llama.cpp
- Implements dynamic FPS sampling for video processing
- Features mRoPE temporal alignment for video understanding
Core Capabilities
- Advanced visual recognition of objects, texts, charts, and layouts
- Long video understanding (>1 hour) with temporal event capturing
- Visual localization with bounding box and point generation
- Structured output generation for documents and forms
- Agent-like capabilities for computer and phone use scenarios
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its ability to process both images and long videos while maintaining high performance in a compact 3B parameter size. It features advanced temporal understanding and structured output capabilities, making it versatile for various applications.
Q: What are the recommended use cases?
The model excels in document analysis, video understanding, visual recognition tasks, and agent-based interactions. It's particularly suitable for applications requiring both image and video processing capabilities with limited computational resources.
